"""Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.

Each stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt
template from disk, calls the LLM client, parses the response, writes
results back, and updates processing_status on SourceVideo.

Celery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.
"""

from __future__ import annotations

import json
import logging
import time
from collections import defaultdict
from pathlib import Path

import yaml
from celery import chain as celery_chain
from pydantic import ValidationError
from sqlalchemy import create_engine, select
from sqlalchemy.orm import Session, sessionmaker

from config import get_settings
from models import (
    KeyMoment,
    KeyMomentContentType,
    ProcessingStatus,
    SourceVideo,
    TechniquePage,
    TranscriptSegment,
)
from pipeline.embedding_client import EmbeddingClient
from pipeline.llm_client import LLMClient
from pipeline.qdrant_client import QdrantManager
from pipeline.schemas import (
    ClassificationResult,
    ExtractionResult,
    SegmentationResult,
    SynthesisResult,
)
from worker import celery_app

logger = logging.getLogger(__name__)

# ── Helpers ──────────────────────────────────────────────────────────────────

_engine = None
_SessionLocal = None


def _get_sync_engine():
    """Create a sync SQLAlchemy engine, converting the async URL if needed."""
    global _engine
    if _engine is None:
        settings = get_settings()
        url = settings.database_url
        # Convert async driver to sync driver
        url = url.replace("postgresql+asyncpg://", "postgresql+psycopg2://")
        _engine = create_engine(url, pool_pre_ping=True, pool_size=5, max_overflow=10)
    return _engine


def _get_sync_session() -> Session:
    """Create a sync SQLAlchemy session for Celery tasks."""
    global _SessionLocal
    if _SessionLocal is None:
        _SessionLocal = sessionmaker(bind=_get_sync_engine())
    return _SessionLocal()


def _load_prompt(template_name: str) -> str:
    """Read a prompt template from the prompts directory.

    Raises FileNotFoundError if the template does not exist.
    """
    settings = get_settings()
    path = Path(settings.prompts_path) / template_name
    if not path.exists():
        logger.error("Prompt template not found: %s", path)
        raise FileNotFoundError(f"Prompt template not found: {path}")
    return path.read_text(encoding="utf-8")


def _get_llm_client() -> LLMClient:
    """Return an LLMClient configured from settings."""
    return LLMClient(get_settings())


def _load_canonical_tags() -> dict:
    """Load canonical tag taxonomy from config/canonical_tags.yaml."""
    # Walk up from backend/ to find config/
    candidates = [
        Path("config/canonical_tags.yaml"),
        Path("../config/canonical_tags.yaml"),
    ]
    for candidate in candidates:
        if candidate.exists():
            with open(candidate, encoding="utf-8") as f:
                return yaml.safe_load(f)
    raise FileNotFoundError(
        "canonical_tags.yaml not found. Searched: " + ", ".join(str(c) for c in candidates)
    )


def _format_taxonomy_for_prompt(tags_data: dict) -> str:
    """Format the canonical tags taxonomy as readable text for the LLM prompt."""
    lines = []
    for cat in tags_data.get("categories", []):
        lines.append(f"Category: {cat['name']}")
        lines.append(f"  Description: {cat['description']}")
        lines.append(f"  Sub-topics: {', '.join(cat.get('sub_topics', []))}")
        lines.append("")
    return "\n".join(lines)


def _safe_parse_llm_response(raw: str, model_cls, llm: LLMClient, system_prompt: str, user_prompt: str):
    """Parse LLM response with one retry on failure.

    On malformed response: log the raw text, retry once with a JSON nudge,
    then raise on second failure.
    """
    try:
        return llm.parse_response(raw, model_cls)
    except (ValidationError, ValueError, json.JSONDecodeError) as exc:
        logger.warning(
            "First parse attempt failed for %s (%s). Retrying with JSON nudge. "
            "Raw response (first 500 chars): %.500s",
            model_cls.__name__,
            type(exc).__name__,
            raw,
        )
        # Retry with explicit JSON instruction
        nudge_prompt = user_prompt + "\n\nIMPORTANT: Output ONLY valid JSON. No markdown, no explanation."
        retry_raw = llm.complete(system_prompt, nudge_prompt, response_model=model_cls)
        return llm.parse_response(retry_raw, model_cls)


# ── Stage 2: Segmentation ───────────────────────────────────────────────────

@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)
def stage2_segmentation(self, video_id: str) -> str:
    """Analyze transcript segments and identify topic boundaries.

    Loads all TranscriptSegment rows for the video, sends them to the LLM
    for topic boundary detection, and updates topic_label on each segment.

    Returns the video_id for chain compatibility.
    """
    start = time.monotonic()
    logger.info("Stage 2 (segmentation) starting for video_id=%s", video_id)

    session = _get_sync_session()
    try:
        # Load segments ordered by index
        segments = (
            session.execute(
                select(TranscriptSegment)
                .where(TranscriptSegment.source_video_id == video_id)
                .order_by(TranscriptSegment.segment_index)
            )
            .scalars()
            .all()
        )

        if not segments:
            logger.info("Stage 2: No segments found for video_id=%s, skipping.", video_id)
            return video_id

        # Build transcript text with indices for the LLM
        transcript_lines = []
        for seg in segments:
            transcript_lines.append(
                f"[{seg.segment_index}] ({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}"
            )
        transcript_text = "\n".join(transcript_lines)

        # Load prompt and call LLM
        system_prompt = _load_prompt("stage2_segmentation.txt")
        user_prompt = f"<transcript>\n{transcript_text}\n</transcript>"

        llm = _get_llm_client()
        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult)
        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt)

        # Update topic_label on each segment row
        seg_by_index = {s.segment_index: s for s in segments}
        for topic_seg in result.segments:
            for idx in range(topic_seg.start_index, topic_seg.end_index + 1):
                if idx in seg_by_index:
                    seg_by_index[idx].topic_label = topic_seg.topic_label

        session.commit()
        elapsed = time.monotonic() - start
        logger.info(
            "Stage 2 (segmentation) completed for video_id=%s in %.1fs — %d topic groups found",
            video_id, elapsed, len(result.segments),
        )
        return video_id

    except FileNotFoundError:
        raise  # Don't retry missing prompt files
    except Exception as exc:
        session.rollback()
        logger.error("Stage 2 failed for video_id=%s: %s", video_id, exc)
        raise self.retry(exc=exc)
    finally:
        session.close()


# ── Stage 3: Extraction ─────────────────────────────────────────────────────

@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)
def stage3_extraction(self, video_id: str) -> str:
    """Extract key moments from each topic segment group.

    Groups segments by topic_label, calls the LLM for each group to extract
    moments, creates KeyMoment rows, and sets processing_status=extracted.

    Returns the video_id for chain compatibility.
    """
    start = time.monotonic()
    logger.info("Stage 3 (extraction) starting for video_id=%s", video_id)

    session = _get_sync_session()
    try:
        # Load segments with topic labels
        segments = (
            session.execute(
                select(TranscriptSegment)
                .where(TranscriptSegment.source_video_id == video_id)
                .order_by(TranscriptSegment.segment_index)
            )
            .scalars()
            .all()
        )

        if not segments:
            logger.info("Stage 3: No segments found for video_id=%s, skipping.", video_id)
            return video_id

        # Group segments by topic_label
        groups: dict[str, list[TranscriptSegment]] = defaultdict(list)
        for seg in segments:
            label = seg.topic_label or "unlabeled"
            groups[label].append(seg)

        system_prompt = _load_prompt("stage3_extraction.txt")
        llm = _get_llm_client()
        total_moments = 0

        for topic_label, group_segs in groups.items():
            # Build segment text for this group
            seg_lines = []
            for seg in group_segs:
                seg_lines.append(
                    f"({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}"
                )
            segment_text = "\n".join(seg_lines)

            user_prompt = (
                f"Topic: {topic_label}\n\n"
                f"<segment>\n{segment_text}\n</segment>"
            )

            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult)
            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt)

            # Create KeyMoment rows
            for moment in result.moments:
                # Validate content_type against enum
                try:
                    ct = KeyMomentContentType(moment.content_type)
                except ValueError:
                    ct = KeyMomentContentType.technique

                km = KeyMoment(
                    source_video_id=video_id,
                    title=moment.title,
                    summary=moment.summary,
                    start_time=moment.start_time,
                    end_time=moment.end_time,
                    content_type=ct,
                    plugins=moment.plugins if moment.plugins else None,
                    raw_transcript=moment.raw_transcript or None,
                )
                session.add(km)
                total_moments += 1

        # Update processing_status to extracted
        video = session.execute(
            select(SourceVideo).where(SourceVideo.id == video_id)
        ).scalar_one()
        video.processing_status = ProcessingStatus.extracted

        session.commit()
        elapsed = time.monotonic() - start
        logger.info(
            "Stage 3 (extraction) completed for video_id=%s in %.1fs — %d moments created",
            video_id, elapsed, total_moments,
        )
        return video_id

    except FileNotFoundError:
        raise
    except Exception as exc:
        session.rollback()
        logger.error("Stage 3 failed for video_id=%s: %s", video_id, exc)
        raise self.retry(exc=exc)
    finally:
        session.close()


# ── Stage 4: Classification ─────────────────────────────────────────────────

@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)
def stage4_classification(self, video_id: str) -> str:
    """Classify key moments against the canonical tag taxonomy.

    Loads all KeyMoment rows for the video, sends them to the LLM with the
    canonical taxonomy, and stores classification results in Redis for
    stage 5 consumption. Updates content_type if the classifier overrides it.

    Stage 4 does NOT change processing_status.

    Returns the video_id for chain compatibility.
    """
    start = time.monotonic()
    logger.info("Stage 4 (classification) starting for video_id=%s", video_id)

    session = _get_sync_session()
    try:
        # Load key moments
        moments = (
            session.execute(
                select(KeyMoment)
                .where(KeyMoment.source_video_id == video_id)
                .order_by(KeyMoment.start_time)
            )
            .scalars()
            .all()
        )

        if not moments:
            logger.info("Stage 4: No moments found for video_id=%s, skipping.", video_id)
            # Store empty classification data
            _store_classification_data(video_id, [])
            return video_id

        # Load canonical tags
        tags_data = _load_canonical_tags()
        taxonomy_text = _format_taxonomy_for_prompt(tags_data)

        # Build moments text for the LLM
        moments_lines = []
        for i, m in enumerate(moments):
            moments_lines.append(
                f"[{i}] Title: {m.title}\n"
                f"    Summary: {m.summary}\n"
                f"    Content type: {m.content_type.value}\n"
                f"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}"
            )
        moments_text = "\n\n".join(moments_lines)

        system_prompt = _load_prompt("stage4_classification.txt")
        user_prompt = (
            f"<taxonomy>\n{taxonomy_text}\n</taxonomy>\n\n"
            f"<moments>\n{moments_text}\n</moments>"
        )

        llm = _get_llm_client()
        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult)
        result = _safe_parse_llm_response(raw, ClassificationResult, llm, system_prompt, user_prompt)

        # Apply content_type overrides and prepare classification data for stage 5
        classification_data = []
        moment_ids = [str(m.id) for m in moments]

        for cls in result.classifications:
            if 0 <= cls.moment_index < len(moments):
                moment = moments[cls.moment_index]

                # Apply content_type override if provided
                if cls.content_type_override:
                    try:
                        moment.content_type = KeyMomentContentType(cls.content_type_override)
                    except ValueError:
                        pass

                classification_data.append({
                    "moment_id": str(moment.id),
                    "topic_category": cls.topic_category,
                    "topic_tags": cls.topic_tags,
                })

        session.commit()

        # Store classification data in Redis for stage 5
        _store_classification_data(video_id, classification_data)

        elapsed = time.monotonic() - start
        logger.info(
            "Stage 4 (classification) completed for video_id=%s in %.1fs — %d moments classified",
            video_id, elapsed, len(classification_data),
        )
        return video_id

    except FileNotFoundError:
        raise
    except Exception as exc:
        session.rollback()
        logger.error("Stage 4 failed for video_id=%s: %s", video_id, exc)
        raise self.retry(exc=exc)
    finally:
        session.close()


def _store_classification_data(video_id: str, data: list[dict]) -> None:
    """Store classification data in Redis for cross-stage communication."""
    import redis

    settings = get_settings()
    r = redis.Redis.from_url(settings.redis_url)
    key = f"chrysopedia:classification:{video_id}"
    r.set(key, json.dumps(data), ex=86400)  # Expire after 24 hours


def _load_classification_data(video_id: str) -> list[dict]:
    """Load classification data from Redis."""
    import redis

    settings = get_settings()
    r = redis.Redis.from_url(settings.redis_url)
    key = f"chrysopedia:classification:{video_id}"
    raw = r.get(key)
    if raw is None:
        return []
    return json.loads(raw)


# ── Stage 5: Synthesis ───────────────────────────────────────────────────────

@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)
def stage5_synthesis(self, video_id: str) -> str:
    """Synthesize technique pages from classified key moments.

    Groups moments by (creator, topic_category), calls the LLM to synthesize
    each group into a TechniquePage, creates/updates page rows, and links
    KeyMoments to their TechniquePage.

    Sets processing_status to 'reviewed' (or 'published' if review_mode is False).

    Returns the video_id for chain compatibility.
    """
    start = time.monotonic()
    logger.info("Stage 5 (synthesis) starting for video_id=%s", video_id)

    settings = get_settings()
    session = _get_sync_session()
    try:
        # Load video and moments
        video = session.execute(
            select(SourceVideo).where(SourceVideo.id == video_id)
        ).scalar_one()

        moments = (
            session.execute(
                select(KeyMoment)
                .where(KeyMoment.source_video_id == video_id)
                .order_by(KeyMoment.start_time)
            )
            .scalars()
            .all()
        )

        if not moments:
            logger.info("Stage 5: No moments found for video_id=%s, skipping.", video_id)
            return video_id

        # Load classification data from stage 4
        classification_data = _load_classification_data(video_id)
        cls_by_moment_id = {c["moment_id"]: c for c in classification_data}

        # Group moments by topic_category (from classification)
        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)
        for moment in moments:
            cls_info = cls_by_moment_id.get(str(moment.id), {})
            category = cls_info.get("topic_category", "Uncategorized")
            groups[category].append((moment, cls_info))

        system_prompt = _load_prompt("stage5_synthesis.txt")
        llm = _get_llm_client()
        pages_created = 0

        for category, moment_group in groups.items():
            # Build moments text for the LLM
            moments_lines = []
            all_tags: set[str] = set()
            for i, (m, cls_info) in enumerate(moment_group):
                tags = cls_info.get("topic_tags", [])
                all_tags.update(tags)
                moments_lines.append(
                    f"[{i}] Title: {m.title}\n"
                    f"    Summary: {m.summary}\n"
                    f"    Content type: {m.content_type.value}\n"
                    f"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\n"
                    f"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\n"
                    f"    Category: {category}\n"
                    f"    Tags: {', '.join(tags) if tags else 'none'}\n"
                    f"    Transcript excerpt: {(m.raw_transcript or '')[:300]}"
                )
            moments_text = "\n\n".join(moments_lines)

            user_prompt = f"<moments>\n{moments_text}\n</moments>"

            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult)
            result = _safe_parse_llm_response(raw, SynthesisResult, llm, system_prompt, user_prompt)

            # Create/update TechniquePage rows
            for page_data in result.pages:
                # Check if page with this slug already exists
                existing = session.execute(
                    select(TechniquePage).where(TechniquePage.slug == page_data.slug)
                ).scalar_one_or_none()

                if existing:
                    # Update existing page
                    existing.title = page_data.title
                    existing.summary = page_data.summary
                    existing.body_sections = page_data.body_sections
                    existing.signal_chains = page_data.signal_chains
                    existing.plugins = page_data.plugins if page_data.plugins else None
                    existing.topic_tags = list(all_tags) if all_tags else None
                    existing.source_quality = page_data.source_quality
                    page = existing
                else:
                    page = TechniquePage(
                        creator_id=video.creator_id,
                        title=page_data.title,
                        slug=page_data.slug,
                        topic_category=page_data.topic_category or category,
                        topic_tags=list(all_tags) if all_tags else None,
                        summary=page_data.summary,
                        body_sections=page_data.body_sections,
                        signal_chains=page_data.signal_chains,
                        plugins=page_data.plugins if page_data.plugins else None,
                        source_quality=page_data.source_quality,
                    )
                    session.add(page)
                    session.flush()  # Get the page.id assigned

                pages_created += 1

                # Link moments to the technique page
                for m, _ in moment_group:
                    m.technique_page_id = page.id

        # Update processing_status
        if settings.review_mode:
            video.processing_status = ProcessingStatus.reviewed
        else:
            video.processing_status = ProcessingStatus.published

        session.commit()
        elapsed = time.monotonic() - start
        logger.info(
            "Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated",
            video_id, elapsed, pages_created,
        )
        return video_id

    except FileNotFoundError:
        raise
    except Exception as exc:
        session.rollback()
        logger.error("Stage 5 failed for video_id=%s: %s", video_id, exc)
        raise self.retry(exc=exc)
    finally:
        session.close()


# ── Stage 6: Embed & Index ───────────────────────────────────────────────────

@celery_app.task(bind=True, max_retries=0)
def stage6_embed_and_index(self, video_id: str) -> str:
    """Generate embeddings for technique pages and key moments, then upsert to Qdrant.

    This is a non-blocking side-effect stage — failures are logged but do not
    fail the pipeline. Embeddings can be regenerated later. Does NOT update
    processing_status.

    Returns the video_id for chain compatibility.
    """
    start = time.monotonic()
    logger.info("Stage 6 (embed & index) starting for video_id=%s", video_id)

    settings = get_settings()
    session = _get_sync_session()
    try:
        # Load technique pages created for this video's moments
        moments = (
            session.execute(
                select(KeyMoment)
                .where(KeyMoment.source_video_id == video_id)
                .order_by(KeyMoment.start_time)
            )
            .scalars()
            .all()
        )

        # Get unique technique page IDs from moments
        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}
        pages = []
        if page_ids:
            pages = (
                session.execute(
                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))
                )
                .scalars()
                .all()
            )

        if not moments and not pages:
            logger.info("Stage 6: No moments or pages for video_id=%s, skipping.", video_id)
            return video_id

        embed_client = EmbeddingClient(settings)
        qdrant = QdrantManager(settings)

        # Ensure collection exists before upserting
        qdrant.ensure_collection()

        # ── Embed & upsert technique pages ───────────────────────────────
        if pages:
            page_texts = []
            page_dicts = []
            for p in pages:
                text = f"{p.title} {p.summary or ''} {p.topic_category or ''}"
                page_texts.append(text.strip())
                page_dicts.append({
                    "page_id": str(p.id),
                    "creator_id": str(p.creator_id),
                    "title": p.title,
                    "topic_category": p.topic_category or "",
                    "topic_tags": p.topic_tags or [],
                    "summary": p.summary or "",
                })

            page_vectors = embed_client.embed(page_texts)
            if page_vectors:
                qdrant.upsert_technique_pages(page_dicts, page_vectors)
                logger.info(
                    "Stage 6: Upserted %d technique page vectors for video_id=%s",
                    len(page_vectors), video_id,
                )
            else:
                logger.warning(
                    "Stage 6: Embedding returned empty for %d technique pages (video_id=%s). "
                    "Skipping page upsert.",
                    len(page_texts), video_id,
                )

        # ── Embed & upsert key moments ───────────────────────────────────
        if moments:
            moment_texts = []
            moment_dicts = []
            for m in moments:
                text = f"{m.title} {m.summary or ''}"
                moment_texts.append(text.strip())
                moment_dicts.append({
                    "moment_id": str(m.id),
                    "source_video_id": str(m.source_video_id),
                    "title": m.title,
                    "start_time": m.start_time,
                    "end_time": m.end_time,
                    "content_type": m.content_type.value,
                })

            moment_vectors = embed_client.embed(moment_texts)
            if moment_vectors:
                qdrant.upsert_key_moments(moment_dicts, moment_vectors)
                logger.info(
                    "Stage 6: Upserted %d key moment vectors for video_id=%s",
                    len(moment_vectors), video_id,
                )
            else:
                logger.warning(
                    "Stage 6: Embedding returned empty for %d key moments (video_id=%s). "
                    "Skipping moment upsert.",
                    len(moment_texts), video_id,
                )

        elapsed = time.monotonic() - start
        logger.info(
            "Stage 6 (embed & index) completed for video_id=%s in %.1fs — "
            "%d pages, %d moments processed",
            video_id, elapsed, len(pages), len(moments),
        )
        return video_id

    except Exception as exc:
        # Non-blocking: log error but don't fail the pipeline
        logger.error(
            "Stage 6 failed for video_id=%s: %s. "
            "Pipeline continues — embeddings can be regenerated later.",
            video_id, exc,
        )
        return video_id
    finally:
        session.close()


# ── Orchestrator ─────────────────────────────────────────────────────────────

@celery_app.task
def run_pipeline(video_id: str) -> str:
    """Orchestrate the full pipeline (stages 2-5) with resumability.

    Checks the current processing_status of the video and chains only the
    stages that still need to run. For example:
      - pending/transcribed → stages 2, 3, 4, 5
      - extracted → stages 4, 5
      - reviewed/published → no-op

    Returns the video_id.
    """
    logger.info("run_pipeline starting for video_id=%s", video_id)

    session = _get_sync_session()
    try:
        video = session.execute(
            select(SourceVideo).where(SourceVideo.id == video_id)
        ).scalar_one_or_none()

        if video is None:
            logger.error("run_pipeline: video_id=%s not found", video_id)
            raise ValueError(f"Video not found: {video_id}")

        status = video.processing_status
        logger.info(
            "run_pipeline: video_id=%s current status=%s", video_id, status.value
        )
    finally:
        session.close()

    # Build the chain based on current status
    stages = []
    if status in (ProcessingStatus.pending, ProcessingStatus.transcribed):
        stages = [
            stage2_segmentation.s(video_id),
            stage3_extraction.s(),  # receives video_id from previous
            stage4_classification.s(),
            stage5_synthesis.s(),
            stage6_embed_and_index.s(),
        ]
    elif status == ProcessingStatus.extracted:
        stages = [
            stage4_classification.s(video_id),
            stage5_synthesis.s(),
            stage6_embed_and_index.s(),
        ]
    elif status in (ProcessingStatus.reviewed, ProcessingStatus.published):
        logger.info(
            "run_pipeline: video_id=%s already at status=%s, nothing to do.",
            video_id, status.value,
        )
        return video_id

    if stages:
        pipeline = celery_chain(*stages)
        pipeline.apply_async()
        logger.info(
            "run_pipeline: dispatched %d stages for video_id=%s",
            len(stages), video_id,
        )

    return video_id